International Symposium Geotunis 2009 16-20 December, 2009

1. Integrated GIS and Remote Sensing for Mapping Groundwater Potential Zones in Tulul al Ashaqif Highlands, NE Jordan International Symposium Geotunis 2009 16-20 December, 2009 Muheeb M. Awawdeh & Mohammed Al- Mohammed Department of Earth and Environmental Sciences, Yarmouk University, Irbid 21163, Jordan E-mail: awawdeh@yu.edu.jo

2. INTRODUCTION ranks as one of the world’s 4 most water stressed countries only 170 m3 per capita per year and predicted to be lower than 91 m3 per capita per year by the 2025 climatic conditions (e.g. aridity and abundance of high solar radiation), population pressure, and urban development attention is now focusing on alternatives e.g. finding new groundwater resources and rainwater harvesting systems

3. OBJECTIVES 1-To delineate the groundwater potential zones using relevant data (rainfall, topography, geology, soil, etc.) 2-To develop a GIS model that can identify groundwater potential zones based on the thematic maps 3-To validate the results of this study with data from the field

4. Remote sensing and GIS techniques are one of the surface methods used for groundwater exploration • based on an indirect analysis of some directly observable terrain features • With remotely sensed data and GIS, numerous databases can be integrated to produce conceptual model for delineation and evaluation of groundwater potential zones

5. Study Area: Tulul al Ashaqif highlands • a NW-SE ridge, part of the Badia region, NE Jordan • 660 m -1050 m asl • arid, and erratic rainfall spatially and Temporally with annual average 60-100 mm/yr

6. the ridge defines the boundary between the Azraq and the Hamad hydrographic basins • the ridge is of volcanic origin and Neogene in age

7. the Tulul is characterized by distinct topographic features defined by volcanic cones and river valleys (wadis)

8. largely covered by pavement overlying an eolian sedimentary mantl(

9. Database Construction Remote Sensing Data Topography Lithology Soil Rainfall Data Preparation (Reprojection, Moasicking, Clipping, etc) Digitizing Data Processing (PCA, Band Rationing, Edge Enhancement) Editing & Spatial Adjustment Attribute Data Editing Digitizing Supervised Classification Topography Digitizing Contours TIN Map Drainage System Map Lineaments Map Slope Map Lineaments Density Map Drainage Density Map Geomorphology Map Soil Map Lithology Map Rainfall Map DEM Spatial Data Analysis Data Classification (Rating) Data Modeling Data Modeling Groundwater Potential Map METHODOLOGY

10. 8 thematic layers are selected: • geomorphology, soil texture, lithology, elevation, slope, annual rainfall, drainage density, and lineament density • thematic layers were combined using weight index overlay method • weights assigned to the data layers to reflect their relative importance • determined using analytical hierarchy principle (AHP) • classes in each theme arranged in decreasing order of rating (0-100) based on previous work and experts

11. Weights and ratings

15. Modelling The groundwater potential index value: GPM=(Lw*Lr)+(Gw*Gr)+(Sw*Sr)+(LDw*LDr)+ (Dw*Dr)+(Ew*Er)+(SLw*SLr)+(Rw*Rr) Where, L = Lithology , G=Geomorpholgy, S= Soil, LD=Lineament Density, D=Drainage Density, E= Elevation, SL=Slope, R= Annual Rainfall, W= parameter weight, r= rating.

16. Results and Discussion

17. 1. Lineaments Density

18. 2- Drainage

19. 3. Topography

20. 4. Slope

21. 5. Soil

23. 6. Geomorphology

24. Marabs are broad reaches filled with coarse sand and gravel typically have a relatively rich vegetative cover • Muflats are fine-grained playa deposits that are almost totally devoid of vegetative cover

25. 7. Lithology

27. 8. Rainfall

28. Groundwater Potential Model (GPM)

29. Model Validation

30. Sensitivity Analysis of GPM • map removal sensitivity analysis. Variation index of the excluded parameter

31. 2. single parameter sensitivity analysis. Statistical analysis of the effective weights

32. GPM-effective weights GPM- theoretical weights

33. GPM classes Theoretical weights vs Effective weights

34. CONCLUSIONS • 1. Remote sensing images were very important input to groundwater exploration • -the aridity and sparseness of vegetation in the • study area • -mapping of drainage from satellite imagery is • more effective than the automated derivation by • the GIS software • 2. Most of the very high potential areas represented stream channels and wadi sediments

35. 3. Most of the promising areas are found below 800 m in elevation 4. Sensitivity analysis indicates that all parameters are significant but the most effective parameters : lineaments density, geomorphology, drainage density and annual rainfall 5. Field data were valuable in validating the GPM output. 6. The model identified several locations suitable for further field geophysical investigation

36. RECOMMENDATIONS • To apply the methodologies developed by this research in similar environmental settings in Jordan • 2. Using limited data in groundwater prospecting may result in misleading results, therefore, the combination of many types of data is recommended • 3. Using highly accurate data is critical in producing reliable results.

37. 4. For further validation field geophysical investigations on the potential drilling sites are recommended 6. Landsat images (low cost and wealth of information) justify its use for large study areas 7. GIS and remote sensing systems are cost effective techniques in investigating groundwater potential zones

38. THANK YOU

39. Weights of parameters determined using analytical hierarchy principle (AHP) nine point scale in which a paired comparison matrix was composed The values 2,4,6 and 8 can be used to denote intermediary values.

40. Paired comparison matrix